In this work, we choose to focus on Factorization Machines (FMs) [1]}, classically used for context-aware recommendation.
FMs are a tried-and-true approach to context-aware recommendation, which allow easy integration of side information via extension of the user-item vector.
The advantage of FMs is that we can easily implement two recommender systems, one with and one without user attributes, and be confident that the use of the user attributes in the training data is the only difference between them.
